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Abstract The traditional use of land for food, fuel and wood created cultural landscapes, which are threatened across Europe. The factors which contributed to their endangerment need to be identified to achieve effective preservation of such landscapes. The aim of our study was to identify landscapes with historical persistence in a GIS-based comparative analysis of historical and contemporary maps and the most prominent causes of the past landscape changes, based on stakeholders' perspective. We considered a case study in Romania's Carpathians. Three major land cover types were extracted from maps dating from 1912, 1980 and 2009: built-up, pastures and forests. The historical persistence of all land cover types was poor (

The European Landscape Convention (ELC) defines landscape as "an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors". Because all aspects linked to landscape are evaluated during the Environmental Impact Assessments (EIAs) procedure, we assessed the Swiss and Romanian experts' perception and EIA reports regarding the landscape concept. Therefore, we analysed Swiss and Romanian experts' perception of landscape, we evaluated concepts and methods used in environmental impact reports to assess landscape characteristics and functions, and how the ELC's aim and objectives are reflected by the EIA reports. The Canonical Correspondence Analysis performed to evaluate the experts' perception of landscape integration in the EIA procedure showed a significant relationship among pattern of answers and experts' professional background. Moreover, there is a significant difference between Swiss and Romanian expert's perception. Longer experience in the field and a higher level of education indicates higher awareness of landscape characteristics and properties, which demonstrates that alongside with the initial training, there is a need for developing a coherent long-life learning systems having landscape analysis as a focus. The ELC is familiar to nearly 86% of the Romanian Environmental Impact Assessment actors and nearly 29% of the Swiss experts. By contrast, most of the Romanian EIA reports analysed do not employ many concepts of the ELC, while most of the Swiss environmental reports incorporate objectives of landscape protection consistent with the Convention. Our study relies on the comparative investigation of representative case studies of Environmental Impact Assessment reports for industry, local infrastructures, tourism facilities, and wind farm projects, as well on tracking how the European Landscape Convention is reflected by the EIA reports. We found that EIA reports from both countries evaluate the visual landscape, thus, achieving subjective assessment of landscape aesthetics without focusing on social and ecological sub-systems. Quantitative elements of landscape analysis are mostly absent and it is challenging to evaluate whether environmental assessments are consistent among European Landscape Convention countries. To overcome the abstract way of evaluation of project's impact on landscape through EIA procedure, an improved landscape analysis procedure is needed and we propose developing technical guidelines, under the European Landscape Convention umbrella, in order to enhance landscape management. (C) 2014 Elsevier Ltd. All rights reserved.

We present a novel method for building a multiresolution representation of large digital surface models. The surface points coincide with the nodes of a planar graph which can be processed using a critically sampled, invertible lifting scheme. To drive the lazy wavelet node partitioning, we employ an attribute aware cost function based on the generalized quadric error metric. The resulting algorithm can be applied to multivariate data by storing additional attributes at the graph's nodes. We discuss how the cost computation mechanism can be coupled with the lifting scheme and examine the results by evaluating the root mean square error. The algorithm is experimentally tested using two multivariate LiDAR sets representing terrain surface and vegetation structure with different sampling densities.

AbstractTerrain data can be processed from the double perspective of computer graphics and graph theory. We propose a hybrid method that uses geometrical and vertex attribute information to construct a weighted graph reflecting the variability of the vertex data. As a planar graph, a generic terrain data set is subjected to a geometry‐sensitive vertex partitioning procedure. Through the use of a combined, thin‐plate energy and multi‐dimensional quadric metric error, feature estimation heuristic, we construct ‘even’ and ‘odd’ node subsets. Using an invertible lifting scheme, adapted from generic weighted graphs, detail vectors are extracted and used to recover or filter the node information. The design of the prediction and update filters improves the root mean squared error of the signal over general graph‐based approaches. As a key property of this design, preserving the mean of the graph signal becomes essential for decreasing the error measure and conserving the salient shape features.

See-through dye-sensitized solar cells with 1D photonic crystal Bragg reflector photoanodes show an increase in peak external quantum efficiency of 47% while still maintaining high fill factors, resulting in an almost 40% increase in power conversion efficiency. These photoanodes are ideally suited for tandem and building integrated photovoltaics.